Motion‐corrected and high‐resolution anatomically assisted (MOCHA) reconstruction of arterial spin labeling MRI

PURPOSE: A model‐based reconstruction framework is proposed for motion‐corrected and high‐resolution anatomically assisted (MOCHA) reconstruction of arterial spin labeling (ASL) data. In this framework, all low‐resolution ASL control‐label pairs are used to reconstruct a single high‐resolution cerebral blood flow (CBF) map, corrected for rigid‐motion, point‐spread‐function blurring and partial volume effect. METHODS: Six volunteers were recruited for CBF imaging using pseudo‐continuous ASL labeling, two‐shot 3D gradient and spin‐echo sequences and high‐resolution T(1)‐weighted MRI. For 2 volunteers, high‐resolution scans with double and triple resolution in the partition direction were additionally collected. Simulations were designed for evaluations against a high‐resolution ground‐truth CBF map, including a simulated hyperperfused lesion and hyperperfusion/hypoperfusion abnormalities. The MOCHA technique was compared with standard reconstruction and a 3D linear regression partial‐volume effect correction method and was further evaluated for acquisitions with reduced control‐label pairs and k‐space undersampling. RESULTS: The MOCHA reconstructions of low‐resolution ASL data showed enhanced image quality, particularly in the partition direction. In simulations, both MOCHA and 3D linear regression provided more accurate CBF maps than the standard reconstruction; however, MOCHA resulted in the lowest errors and well delineated the abnormalities. The MOCHA reconstruction of standard‐resolution in vivo data showed good agreement with higher‐resolution scans requiring 4‐times and 9‐times longer acquisitions. The MOCHA reconstruction was found to be robust for 4‐times‐accelerated ASL acquisitions, achieved by reduced control‐label pairs or k‐space undersampling. CONCLUSION: The MOCHA reconstruction reduces partial‐volume effect by direct reconstruction of CBF maps in the high‐resolution space of the corresponding anatomical image, incorporating motion correction and point spread function modeling. Following further evaluation, MOCHA should promote the clinical application of ASL.